This track equips learners to proficiently create, integrate, test, and deploy AI-driven applications and prompt engineering solutions, leveraging modern AI models and real-life cloud environments. This is a back-end oriented project.
Project Definition
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MVP
Timeline
Week | Topic | Project Tasks | Learning (Read/Watch/Exercise) | Completed |
1 | Define project idea and get it approved by Role Expert by EOW. | Watch:
A Hackers' Guide to Language Models
Jay's Intro to AI
How GPT3 Works
Generative AI and AI Product Moats
Large Language Models in Five Formulas | ||
2 | Initialize API and Database.
Code quality and code format | - setup a server
- setup a database
- setup a GitHub repository and connect the project to it. add Git Ignore.
- Create an ENV file to store sensitive data (database API Key)
- connect server to database
- create first API endpoints to test all CRUD operations for one collection in the database.
- Use PyLint to ensure a high level of code quality.
- Use prettier to ensure alignment in code formatting. | AI APIs
ā¢ š« Prompt Engineering Courses
- Coursera
- deeplearning.ai
| |
3 | AI Proof of concept | - Proof of Concept with LLM
- Create a comparison table between the LLMs in our case study and reach a recommendation of what to use.
- Define a UML/diagram of the flow of prompts.
- Choosing the Gen stack (frameworks, libraries). | ā¢ Langchain
ā¢ LLM Models Integration
š Understanding Chains:
- Types of chains (sequential, parallel, branching).
- Creating and managing chains.
- Use cases for different chain types.
š Exercise - Chatbot (Link)
Integrate pre-trained models (e.g., GPT-3) via LangChain.
- Customizing and fine-tuning models.
- Best practices for model selection and training.
| |
4 | AI | ā¢ Data Preprocessing and Cleaning
Loading Data into LangChain:
Methods for importing and loading data.
Handling different data formats.
Handling Missing Data
Data Normalization and Standa rdization | ||
5-6 | AI | Building Your Project | ā¢ Real life use cases
ā¢ Langchain deployment | |
8 | Deployment, Pipeline automation and Presentation | - Use Husky or an alternative to run all documentation and code quality of format tools on every push to GItHub.
- Deploy a production database.
- Deploy your project to Render.com using production ENV variable that points to another database, not the database you used for testing.
Add Readme to GitHub explaining:
- what is the project, what are the key features
- how to install
- how to run the project locally
- all other relevant commands | ā¢ Basic cloud app deployment | |
V2? | Auth | - Build an auth system using JWTs to allow signup and login.
- add a /me endpoint in the authentication system to allow users to fetch their information (using a JWT).
- Encrypt passwords in the database.
- Add auth-middleware to authenticate user in all relevant API endpoints.
- email should be unique. | * Authentication materials @David L. Rajcher | |
V2? | Unit Testing | - Reach at least 50% test coverage.
- Run unit testing automatically on Commit.
- Run unit tests on commit, and make sure to -commit the code only if all tests are PASS. | ā¢ TDD
ā¢ Unit testing |
MVP requirements
- AI Model Integration: Integrate pre-trained models (e.g., GPT-3) via LangChain.
- Data Handling: Create a pipeline for data preprocessing, cleaning, and preparation.
- Prompt Engineering: Design effective prompts for various use cases using LangChain.
- API Development: Build APIs to integrate AI models (FastAPI / Flask).
- Database: Use a database (e.g., PostgreSQL) to store prompts, user data, and responses.
- Validation and Security: Validate inputs and sanitize data to prevent injection attacks.
- Deployment: Deploy the app on cloud platforms (Vercel / Render) with ENV variables, repo, git, build pipeline.
- No UI: Focus on backend functionalities only.
- Authentication - Minimal version (Firebase).
- Langchain usage.
V2
V2 optional requirements
- Testing
- Data Preprocessing, Embeddings.
- Integrations
- Connect GPT with external end-point that we created
- Connect GPT to Database
- Authentication: Implement authentication using JWT. (Moved here by Alon)
- Caching, Token Optimization
- UI